Opsio - Cloud and AI Solutions
Google Cloud

Google Cloud Platform (GCP) — Data & AI Cloud

GCP environments stall when BigQuery costs spike, GKE clusters drift, and Vertex AI models sit unserved. Opsio's GCP managed services bring SRE discipline to your Google Cloud Platform — optimizing BigQuery slots, operating GKE with Autopilot, and deploying Vertex AI models so your data and engineering teams ship faster without firefighting infrastructure.

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GCP

Managed Services

BigQuery

Analytics Expert

GKE

Certified Ops

24/7

SRE Operations

Google Cloud Partner
BigQuery
GKE
Vertex AI
Cloud Run
Terraform

What is Google Cloud Platform (GCP)?

Google Cloud Platform (GCP) services include the design, deployment, and management of infrastructure and applications on Google's cloud — renowned for BigQuery analytics, Vertex AI machine learning, and GKE Kubernetes orchestration.

Expert Google Cloud Consulting for GCP Managed Services

GCP — the full form is Google Cloud Platform — is Google's enterprise cloud offering with over 150 services for compute, storage, data analytics, machine learning, and networking. GCP leads the industry in serverless data warehousing with BigQuery, container orchestration with GKE, and AI/ML capabilities with Vertex AI. For data-intensive organizations, GCP is often the strongest choice.

Opsio's GCP managed services cover the entire platform: GKE cluster operations with Autopilot mode, BigQuery administration and slot optimization, Vertex AI model serving, Cloud Run serverless containers, Cloud SQL and Spanner databases, and security operations using Security Command Center, Cloud Armor, and Chronicle SIEM — all managed with Google SRE practices including SLOs, SLIs, and error budgets.

Our Google Cloud consulting services help organizations adopt GCP for new workloads or migrate from AWS and Azure. We build GCP DevOps pipelines using Cloud Build, Artifact Registry, and Terraform-based infrastructure-as-code to ensure consistent, repeatable deployments across every environment from development through production.

BigQuery cost management is where most GCP clients need immediate help. We optimize slot reservations, implement materialized views, configure BI Engine caching, and enforce query governance policies. Clients typically reduce BigQuery costs by 30-50% within the first month while improving query performance through partition pruning and clustering strategies.

GKE operations require Kubernetes expertise combined with GCP-specific knowledge. We configure Autopilot for hands-off node management, workload identity for secure pod authentication, network policies for microsegmentation, and horizontal pod autoscaling tuned to your traffic patterns. Our SRE team monitors cluster health around the clock with custom SLO dashboards.

Security on GCP follows a defense-in-depth model. We deploy Security Command Center for vulnerability detection, Cloud Armor for WAF and DDoS protection, VPC Service Controls for data exfiltration prevention, and Chronicle SIEM for threat investigation. Compliance monitoring covers CIS Benchmarks, GDPR, SOC 2, and ISO 27001 with automated policy enforcement using Organization Policy constraints.

GKE Cluster Management & AutopilotGoogle Cloud
BigQuery Analytics PlatformGoogle Cloud
Vertex AI & ML OperationsGoogle Cloud
GCP DevOps & Cloud BuildGoogle Cloud
GCP Security & Chronicle SIEMGoogle Cloud
GCP Cost Optimization & CUDsGoogle Cloud
Google Cloud PartnerGoogle Cloud
BigQueryGoogle Cloud
GKEGoogle Cloud
GKE Cluster Management & AutopilotGoogle Cloud
BigQuery Analytics PlatformGoogle Cloud
Vertex AI & ML OperationsGoogle Cloud
GCP DevOps & Cloud BuildGoogle Cloud
GCP Security & Chronicle SIEMGoogle Cloud
GCP Cost Optimization & CUDsGoogle Cloud
Google Cloud PartnerGoogle Cloud
BigQueryGoogle Cloud
GKEGoogle Cloud
GKE Cluster Management & AutopilotGoogle Cloud
BigQuery Analytics PlatformGoogle Cloud
Vertex AI & ML OperationsGoogle Cloud
GCP DevOps & Cloud BuildGoogle Cloud
GCP Security & Chronicle SIEMGoogle Cloud
GCP Cost Optimization & CUDsGoogle Cloud
Google Cloud PartnerGoogle Cloud
BigQueryGoogle Cloud
GKEGoogle Cloud

How We Compare

CapabilityIn-House TeamOther ProviderOpsio
GCP certifications1-2 certs typicalVaries widelyFull GCP stack certified
BigQuery optimizationAd-hoc query tuningBasic monitoringSlot optimization + 30-50% savings
GKE operationsDeveloper-managedBasic monitoringSRE-operated with SLO dashboards
Vertex AI supportData scientist self-serviceNot offeredProduction MLOps with monitoring
Security postureManual reviewsBasic SCC setupSCC + Chronicle + VPC Controls
Cost governanceMonthly bill reviewQuarterly auditContinuous FinOps with CUD management
Typical annual cost$250K+ (2-3 engineers)$120-180K$60-180K (fully managed)

What We Deliver

GKE Cluster Management & Autopilot

Production GKE operations including Autopilot mode configuration, node pool optimization, workload identity federation, network policies, horizontal pod autoscaling, and GKE Gateway API for ingress. We operate clusters with SRE practices — SLOs, error budgets, and incident management.

BigQuery Analytics Platform

End-to-end BigQuery administration covering dataset design, slot capacity management with reservations and autoscaling, BI Engine caching, materialized views, Looker integration, and cost controls. We typically reduce BigQuery spend by 30-50% through query optimization and governance.

Vertex AI & ML Operations

Production machine learning with Vertex AI including model training pipelines, model registry, online and batch prediction endpoints, feature store management, and GPU quota optimization. We deploy models to production with monitoring, A/B testing, and automated retraining.

GCP DevOps & Cloud Build

Full GCP DevOps services using Cloud Build for CI/CD, Artifact Registry for container and package management, Cloud Deploy for delivery pipelines, and Terraform modules for infrastructure provisioning. Pipeline templates ensure consistency across teams and environments.

GCP Security & Chronicle SIEM

Comprehensive security using Security Command Center for misconfiguration detection, Cloud Armor WAF rules, VPC Service Controls for data exfiltration prevention, and Chronicle SIEM for threat detection. Organization Policy constraints enforce compliance automatically.

GCP Cost Optimization & CUDs

FinOps practice leveraging Committed Use Discounts for compute and databases, sustained use discounts, preemptible and spot VMs, BigQuery slot reservations vs on-demand pricing, and active recommendations from the GCP Recommender API. Monthly cost reports with trend analysis.

Ready to get started?

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What You Get

GCP organization hierarchy with folder structure and IAM policies
VPC network design with shared VPC and private service access
BigQuery data platform with optimized slot reservations and governance
GKE cluster configuration with workload identity and autoscaling
Cloud Build CI/CD pipelines with Terraform infrastructure-as-code
Security Command Center setup with vulnerability and threat detection
Cost optimization audit with CUD and sustained use recommendations
Cloud monitoring dashboards with custom SLOs and error budgets
Monthly operations report with uptime, incident, and cost metrics
Knowledge transfer documentation and team enablement sessions
Opsio has been a reliable partner in managing our cloud infrastructure. Their expertise in security and managed services gives us the confidence to focus on our core business while knowing our IT environment is in good hands.

Magnus Norman

Head of IT, Löfbergs

Investment Overview

Transparent pricing. No hidden fees. Scope-based quotes.

GCP Architecture Assessment

$8,000–$18,000

1-2 week engagement

Most Popular

Migration & Implementation

$25,000–$75,000

Most popular — full migration

Managed GCP Operations

$5,000–$15,000/mo

Ongoing SRE operations

Pricing varies based on scope, complexity, and environment size. Contact us for a tailored quote.

Questions about pricing? Let's discuss your specific requirements.

Get a Custom Quote

Why Choose Opsio

GCP managed services expertise

Certified operations across GKE, BigQuery, Vertex AI, and Cloud Run.

Google SRE practices

SLO, SLI, and error budget discipline applied to your environment.

BigQuery cost specialists

30-50% BigQuery savings through slot optimization and query governance.

GCP DevOps services

Cloud Build, Artifact Registry, and Terraform pipeline automation.

Multi-cloud capability

Unified management alongside AWS and Azure environments.

CUD and cost optimization

Committed Use Discounts, spot VMs, and sustained use management.

Not sure yet? Start with a pilot.

Begin with a focused 2-week assessment. See real results before committing to a full engagement. If you proceed, the pilot cost is credited toward your project.

Our Delivery Process

01

GCP Assessment & Strategy

Review current environment, evaluate GCP service fit, and design target architecture with cost projections. Deliverable: architecture blueprint and migration roadmap. Timeline: 1-2 weeks.

02

Foundation & Security

Deploy organization hierarchy, VPC networking with shared VPC, IAM policies, Security Command Center, logging, and monitoring foundation. Timeline: 2-3 weeks.

03

Migration & DevOps

Execute workload migration with Migrate for Compute Engine, configure BigQuery datasets, and build Cloud Build CI/CD pipelines with Terraform. Timeline: 4-8 weeks.

04

Operate & Innovate

24/7 SRE operations with BigQuery optimization, GKE cluster management, Vertex AI model serving, and continuous cost governance with monthly reporting. Timeline: Ongoing.

Key Takeaways

  • GKE Cluster Management & Autopilot
  • BigQuery Analytics Platform
  • Vertex AI & ML Operations
  • GCP DevOps & Cloud Build
  • GCP Security & Chronicle SIEM

Industries We Serve

Data & Analytics

BigQuery data warehouses with Looker dashboards and Dataflow pipelines.

AI & Machine Learning

Vertex AI production ML workloads with model monitoring and retraining.

SaaS & Technology

GKE and Cloud Run applications with auto-scaling and global load balancing.

Media & Entertainment

Content processing, transcoding, and delivery at global scale.

Google Cloud Platform (GCP) — Data & AI Cloud FAQ

What does GCP stand for — what is the GCP full form?

GCP stands for Google Cloud Platform. It is Google's suite of cloud computing services offering over 150 products across compute, storage, data analytics, machine learning, and networking. GCP is particularly strong in serverless data analytics with BigQuery, container orchestration with GKE, and AI/ML with Vertex AI. The platform is used by organizations that prioritize data-driven decision making and AI capabilities. GCP also offers competitive pricing through sustained use discounts and committed use discounts. Its global fiber network provides low-latency connectivity between regions, making it an excellent choice for globally distributed applications and real-time data processing workloads.

What do Opsio's GCP managed services include?

Our GCP managed services cover 24/7 SRE operations for GKE clusters, BigQuery platform administration and cost optimization, Vertex AI model serving and monitoring, Cloud Run management, security monitoring with Security Command Center and Chronicle SIEM, cost optimization with CUDs and spot VMs, and GCP DevOps services including Cloud Build CI/CD pipelines and Terraform automation. Each service includes SLA-backed response times, proactive monitoring, and monthly reporting. Our GCP-certified engineers manage your environment end-to-end so your internal team can focus on building features and analyzing data rather than managing infrastructure.

How much do Google Cloud consulting services cost?

GCP architecture assessments are a one-time $8,000-$18,000 investment. Migration and implementation projects range from $25,000 to $75,000 depending on scope. Managed GCP operations range from $5,000 to $15,000 per month. Our CUD and BigQuery optimization practice typically reduces GCP spend by 25-40% from on-demand pricing — often covering the management fee. For example, a data-intensive organization spending $30,000 monthly on BigQuery and GKE typically saves $8,000-$12,000 through slot reservations, committed use discounts, and workload right-sizing. We provide transparent monthly cost reports that detail savings by category and identify new optimization opportunities.

How does Opsio optimize BigQuery costs?

We analyze query patterns to determine optimal pricing model — on-demand versus flat-rate slot reservations. We implement materialized views for repeated queries, BI Engine caching for dashboard workloads, partition pruning and clustering for scan reduction, and query governance policies to prevent runaway costs. Clients typically save 30-50% on BigQuery spend within the first month. For example, we identify expensive recurring queries that scan entire tables and add clustering keys or materialized views to reduce bytes processed by 80% or more. We also configure custom quotas per project and user to prevent individual queries from consuming excessive resources unexpectedly.

Can Opsio migrate workloads from AWS or Azure to GCP?

Yes. We use Migrate for Compute Engine for VM migrations, Database Migration Service for MySQL and PostgreSQL, and Transfer Service for storage migrations. Every migration includes application assessment, dependency mapping, rehearsal cutover, and performance validation. We also support multi-cloud architectures where GCP handles analytics while AWS or Azure runs other workloads. A common pattern is migrating data analytics and machine learning workloads to GCP for BigQuery and Vertex AI while keeping application workloads on AWS or Azure. Our cross-cloud networking expertise ensures secure, low-latency connectivity between environments through dedicated interconnects or VPN tunnels.

How does Opsio manage GKE clusters?

We configure GKE Autopilot for hands-off node management or Standard mode with custom node pools for specialized workloads. Operations include workload identity for secure authentication, network policies for microsegmentation, horizontal pod autoscaling, GKE Gateway API for ingress, and custom SLO dashboards. Our SRE team monitors cluster health 24/7 and handles node upgrades, security patches, and capacity planning proactively. For GPU-intensive workloads like machine learning inference, we configure dedicated node pools with appropriate accelerators. GKE's integration with Google's networking infrastructure provides superior pod-to-pod performance compared to other managed Kubernetes platforms in most benchmarks.

What GCP security services does Opsio implement?

We deploy Security Command Center Premium for vulnerability and threat detection, Cloud Armor for WAF and DDoS protection, VPC Service Controls for data exfiltration prevention, Chronicle SIEM for threat investigation, and Organization Policy constraints for compliance enforcement. Security posture is monitored continuously with weekly reporting against CIS, GDPR, and SOC 2 frameworks. For example, VPC Service Controls create security perimeters around sensitive BigQuery datasets and Cloud Storage buckets to prevent data from leaving authorized boundaries. Chronicle's petabyte-scale log analysis enables rapid threat hunting across your entire GCP environment with automated correlation of suspicious activity patterns.

What is the difference between GCP and AWS?

GCP excels in data analytics (BigQuery is unmatched for serverless warehousing), AI/ML (Vertex AI with TPU access), and Kubernetes (GKE originated from Google's internal Borg system). AWS leads in breadth of services (200+ versus 150+) and enterprise ecosystem. Many organizations use both — GCP for data and ML workloads, AWS for general compute and enterprise applications. GCP also offers competitive pricing with sustained use discounts that apply automatically without commitment. The choice depends on your primary workload types, team expertise, and existing vendor relationships. Opsio is certified across both platforms and helps you leverage each provider's strengths effectively.

Does Opsio support Vertex AI for production ML?

Yes. We deploy production ML workloads on Vertex AI including training pipelines, model registry, online and batch prediction endpoints, feature store management, model monitoring for drift detection, and GPU quota optimization. Our MLOps practice integrates Vertex AI with your CI/CD pipelines for automated model deployment and retraining workflows. For example, we configure automated retraining triggers when model performance degrades below defined thresholds, with champion-challenger testing before promoting new model versions. Our monitoring tracks prediction latency, feature distribution drift, and accuracy metrics against ground truth labels to ensure production models maintain their effectiveness over time.

How does Opsio handle GCP cost optimization beyond CUDs?

Beyond Committed Use Discounts, we leverage sustained use discounts for consistent workloads, preemptible and spot VMs for batch processing, BigQuery slot reservations for predictable analytics costs, Cloud Storage lifecycle policies for data tiering, and the GCP Recommender API for proactive right-sizing. Monthly FinOps reports track savings and identify new optimization opportunities. For example, we configure automated lifecycle policies that move infrequently accessed data from Standard to Nearline and eventually Coldline storage, reducing storage costs by up to 80%. We also identify idle resources such as unattached persistent disks and unused static IP addresses that accumulate unnecessary charges over time.

Still have questions? Our team is ready to help.

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Editorial standards: Written by certified cloud practitioners. Peer-reviewed by our engineering team. Updated quarterly.
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Google Cloud Platform (GCP) — Data & AI Cloud

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